import gradio as gr
import requests
import os
import io

API_URL1 = "https://api-inference.huggingface.co/models/cardiffnlp/twitter-roberta-base-sentiment"
API_URL2 = "https://api-inference.huggingface.co/models/facebook/convnext-xlarge-384-22k-1k"
API_URL3 = "https://api-inference.huggingface.co/models/microsoft/trocr-base-handwritten"
API_URL4 = "https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5"


bt = os.environ['HACKAITHONBEARERTOKEN']
headers = {"Authorization": bt }

def query(mood, select_model, filepath):

    print (select_model);
    print (filepath);
    
    
    if (select_model=="Sentiment"):
    	response = requests.post(API_URL1, headers=headers, json=mood)
    elif (select_model=="WhatIsThat"):
        data = open(filepath, 'rb' ).read()
        response = requests.post(API_URL2, headers=headers, data=data)
    elif (select_model=="HandWriting"):
        data = open(filepath, 'rb' ).read()
        response = requests.post(API_URL3, headers=headers, data=data)
    else:
        response = requests.post(API_URL4, headers=headers, json=mood) 
        
    if (select_model=="Sentiment"):
        return str(response.json())
    elif (select_model=="WhatIsThat"):
        return str(response.json())    
    elif (select_model=="HandWriting"):
        return str(response.json())
    else:
        return response.content 

        
def greet(mood,select_model,image):
    output = query({"inputs":mood}, select_model, image)
    
    if (select_model=="Text2Image"):
        from PIL import Image
        image = Image.open(io.BytesIO(output))   
    else:
        print (str(output))
        return str(output)

iface = gr.Interface(
  fn=greet, inputs=["text", gr.Radio(choices=["Sentiment", "WhatIsThat", "HandWriting","Text2Image"],value="Sentiment"),gr.Image(type="filepath")], outputs="image")
iface.launch()